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The New Division of Labor
As the current recession ends, many workers will not be returning to the jobs they once held--those jobs are gone. InThe New Division of Labor, Frank Levy and Richard Murnane show how computers are changing the employment landscape and how the right kinds of education can ease the transition to the new job market.
The book tells stories of people at work--a high-end financial advisor, a customer service representative, a pair of successful chefs, a cardiologist, an automotive mechanic, the author Victor Hugo, floor traders in a London financial exchange. The authors merge these stories with insights from cognitive science, computer science, and economics to show how computers are enhancing productivity in many jobs even as they eliminate other jobs--both directly and by sending work offshore. At greatest risk are jobs that can be expressed in programmable rules--blue collar, clerical, and similar work that requires moderate skills and used to pay middle-class wages. The loss of these jobs leaves a growing division between those who can and cannot earn a good living in the computerized economy. Left unchecked, the division threatens the nation's democratic institutions.
The nation's challenge is to recognize this division and to prepare the population for the high-wage/high-skilled jobs that are rapidly growing in number--jobs involving extensive problem solving and interpersonal communication. Using detailed examples--a second grade classroom, an IBM managerial training program, Cisco Networking Academies--the authors describe how these skills can be taught and how our adjustment to the computerized workplace can begin in earnest.
eBook
Toward systematic review automation: a practical guide to using machine learning tools in research synthesis
Technologies and methods to speed up the production of systematic reviews by reducing the manual labour involved have recently emerged. Automation has been proposed or used to expedite most steps of the systematic review process, including search, screening, and data extraction. However, how these technologies work in practice and when (and when not) to use them is often not clear to practitioners. In this practical guide, we provide an overview of current machine learning methods that have been proposed to expedite evidence synthesis. We also offer guidance on which of these are ready for use, their strengths and weaknesses, and how a systematic review team might go about using them in practice.
Journal Article
Sociotechnical Envelopment of Artificial Intelligence: An Approach to Organizational Deployment of Inscrutable Artificial Intelligence Systems
The paper presents an approach for implementing inscrutable (i.e., nonexplainable) artificial intelligence (AI) such as neural networks in an accountable and safe manner in organizational settings. Drawing on an exploratory case study and the recently proposed concept of envelopment, it describes a case of an organization successfully “enveloping” its AI solutions to balance the performance benefits of flexible AI models with the risks that inscrutable models can entail. The authors present several envelopment methods—establishing clear boundaries within which the AI is to interact with its surroundings, choosing and curating the training data well, and appropriately managing input and output sources—alongside their influence on the choice of AI models within the organization. This work makes two key contributions: It introduces the concept of sociotechnical envelopment by demonstrating the ways in which an organization’s successful AI envelopment depends on the interaction of social and technical factors, thus extending the literature’s focus beyond mere technical issues. Secondly, the empirical examples illustrate how operationalizing a sociotechnical envelopment enables an organization to manage the trade-off between low explainability and high performance presented by inscrutable models. These contributions pave the way for more responsible, accountable AI implementations in organizations, whereby humans can gain better control of even inscrutable machine-learning models.
Journal Article
Public value creation and robotic process automation: normative, descriptive and prescriptive issues in municipal administration
Purpose
This paper aims to highlight problems and opportunities for introducing digital automation in public administration (PA) and to propose implications for public value creation of robotic process automation (RPA) through the perspective of good bureaucracy as a guiding framework.
Design/methodology/approach
This conceptual paper addresses the purpose by applying three normative ideal types: Weber’s ideal type for a bureaucracy, new public management and public value management. This paper synthesizes an analytical framework in conducting case studies of the implementation of RPA systems in municipal administration.
Findings
This paper contributes to new insights into public value creation and digital automation. The following four implications are proposed: the deployment of RPA in municipal administration should emphasize that organizing administrative tasks is essentially a political issue; include considerations based on a well-grounded analysis in which policy areas that are suitable for RPA; to pay attention to issues on legal certainty, personal integrity, transparency and opportunities to influence automated decisions; and that the introduction of RPA indicates a need to develop resources concerning learning and knowledge in the municipal administration.
Originality/value
This paper is innovative, as it relates normative, descriptive and prescriptive issues on the developing of digital automation in PA. The conceptual approach is unusual in studies of digitalization in public activities.
Journal Article
Creating public value through smart technologies and strategies
PurposeThe purpose of this paper is to introduce the special issue about generation of public value through smart technologies and strategies. The key argument is that smart technologies have the potential to foster co-creation of public services and the generation of public value in management processes, based on the collaborative, social and horizontal nature of these smart technologies. Understanding these processes from a public management perspective is the purpose of this paper and the rest of the special issue.Design/methodology/approachThe approach to this paper is a theoretical and conceptual review, whereas practical implications both for scholars and practitioners arise from the review of the literature and the conceptual approximation to the notion of smartness in technologies and government. This approach is rooted in the potential of the latest smart technologies and strategies to transform public administrations and to better understand and cope societal problems.FindingsThe conceptual and theoretical perspective of this paper offers ideas for future developments. The content of this paper shows that new smart technologies and strategies will shape, and will be shaped by, the future of public organizations and management. This paper illustrates the process of change in public value generation over time, as a result of different public management paradigms (from traditional public administration to new public management), but also different types of technologies (from mainframes to websites and social media and beyond). The empirical evidence of the articles of this special issue supports this conclusion; that open and collaborative innovation processes developed under this emergent technological wave could become encouraging transformative practices in the public sector.Research limitations/implicationsThe theoretical and conceptual nature of this paper needs further empirical research to validate some of the discussed assumptions and ideas.Originality/valueAlthough this paper is oriented to present the main contents of the special issue, it also provides an original approach to the theme of public value generation using smart technologies and strategies in public sector management.
Journal Article
Legitimacy of Algorithmic Decision-Making: Six Threats and the Need for a Calibrated Institutional Response
Abstract
Algorithmic decision-making in government has emerged rapidly in recent years, leading to a surge in attention for this topic by scholars from various fields, including public administration. Recent studies provide crucial yet fragmented insights on how the use of algorithms to support or fully automate decisions is transforming government. This article ties together these insights by applying the theoretical lenses of government legitimacy and institutional design. We identify how algorithmic decision-making challenges three types of legitimacy—input, throughput, and output—and identify institutional arrangements that can mitigate these threats. We argue that there is no silver bullet to maintain legitimacy of algorithmic government and that a multiplicity of different institutional mechanisms is required, ranging from legal structures and civic participation to closer monitoring of algorithmic systems. We conclude with a framework to guide future research to better understand the implications of institutional design for the legitimacy of algorithmic government.
Journal Article
The OpenNeuro resource for sharing of neuroscience data
The sharing of research data is essential to ensure reproducibility and maximize the impact of public investments in scientific research. Here, we describe OpenNeuro, a BRAIN Initiative data archive that provides the ability to openly share data from a broad range of brain imaging data types following the FAIR principles for data sharing. We highlight the importance of the Brain Imaging Data Structure standard for enabling effective curation, sharing, and reuse of data. The archive presently shares more than 600 datasets including data from more than 20,000 participants, comprising multiple species and measurement modalities and a broad range of phenotypes. The impact of the shared data is evident in a growing number of published reuses, currently totalling more than 150 publications. We conclude by describing plans for future development and integration with other ongoing open science efforts.
Journal Article
Management of drug-resistant tuberculosis
Drug-resistant tuberculosis is a major public health concern in many countries. Over the past decade, the number of patients infected with Mycobacterium tuberculosis resistant to the most effective drugs against tuberculosis (ie, rifampicin and isoniazid), which is called multidrug-resistant tuberculosis, has continued to increase. Globally, 4·6% of patients with tuberculosis have multidrug-resistant tuberculosis, but in some areas, like Kazakhstan, Kyrgyzstan, Moldova, and Ukraine, this proportion exceeds 25%. Treatment for patients with multidrug-resistant tuberculosis is prolonged (ie, 9–24 months) and patients with multidrug-resistant tuberculosis have less favourable outcomes than those treated for drug-susceptible tuberculosis. Individualised multidrug-resistant tuberculosis treatment with novel (eg, bedaquiline) and repurposed (eg, linezolid, clofazimine, or meropenem) drugs and guided by genotypic and phenotypic drug susceptibility testing can improve treatment outcomes. Some clinical trials are evaluating 6-month regimens to simplify management and improve outcomes of patients with multidrug-resistant tuberculosis. Here we review optimal diagnostic and treatment strategies for patients with drug-resistant tuberculosis and their contacts.
Journal Article
An Integrative Review of Automation Integration in Emergency Nursing Practice: Evidence Synthesis and Contextual Application to Rafidia Governmental Hospital, Palestine
To synthesize evidence on automation integration in emergency nursing and propose a contextually appropriate implementation framework for a governmental hospital in Palestine.
Integrative review.
Following the Whittemore and Knafl methodology, we conducted a comprehensive search across PubMed, CINAHL, Scopus, Web of Science, IEEE Xplore, and regional databases from January 2015 to March 2025. Two independent reviewers screened articles, with conflicts resolved through discussion. The quality of included studies was appraised using the Mixed Methods Appraisal Tool (MMAT). A total of 47 sources were included and analyzed thematically using NVivo 14, guided by sociotechnical systems theory and ethics of care principles.
Five themes emerged: (1) types of automation with proven efficacy in emergency nursing; (2) outcomes including workflow efficiency gains (up to 40% time savings reported in high-resource settings) and safety improvements (30%-50% error reduction); (3) contextual barriers (infrastructure, financial constraints, and workforce readiness) and facilitators (nurse involvement and phased implementation); (4) ethical considerations specific to Arab healthcare contexts; and (5) alignment of local pilot experiences at Rafidia Hospital with global evidence. Key gaps identified include the absence of formal governance, limited evaluation infrastructure, and a lack of embedded sustainability planning, gaps that are particularly consequential in politically constrained, resource-limited environments.
Automation holds significant promise for emergency nursing in Palestine, but success depends on contextual adaptation, nurse-led design, and sustainable planning.
Journal Article